1. Field of the Invention
The present invention pertains to a bioreactor, more particularly, a low volume bioreactor (microbioreactor). Further, the present invention pertains to the use of non-invasive optical chemical sensors to measure multiple parameters in a bioprocessing system.
2. Background of Related Art
Bioprocesses are important in a wide variety of industries such as pharmaceutical, food, ecology and water treatment, as well as to ventures such as the human genome project (Arroyo, M. et al., Biotechnol Prog. 16: 368–371 (2000); Bakoyianis, V. and Koutinas, A. A., Biotechnol Bioeng. 49: 197–203 (1996); Bylund, F. et al., Biotechnol Bioeng. 69: 119–128 (2000); Handa-Corrigan, A. et al., J. Chem. Technol. Biotechnol. 71: 51–56 (1998); López-López, A. et al., Biotechnol Bioeng. 63: 79–86 (1999); McIntyre, J. J. et al., Biotechnol Bioeng. 62: 576–582 (1999); Pressman, J. G. et al., Biotechnol Bioeng. 62: 681–692 (1999); Yang, J.-D. et al., Biotechnol Bioeng. 69: 74–82 (2000)).
The sequencing of the human genome has been a mammoth task, however, many have pointed out that this effort pales in comparison to what lies ahead. The next step is to identify what turns the identified genes on and what proteins these genes express. Cell cultivation will play a critical role in elucidating these factors. More specifically, after the 50,000–100,000 human genes are cloned into various hosts, such as bacteria, yeast and tissue culture cells, an enormous permutation of culture conditions will have to be evaluated to identify the critical factors that turn the genes on. Next, the identity of the proteins produced will have to be determined, thus, efficient production strategies will be needed to obtain enough proteins for crystallographic studies. A highly combinatorial technique could significantly speed up this identification process. Clearly, the ability to culture cells in controlled environments is crucial to this venture so that the benefits of human genome sequencing can be realized.
The ability to control the environment is also important in the area of new drug validation. Bioprocesses associated with new drugs are permitted a window of operating parameters, e.g., temperature, pH, etc., that are based on data obtained during the FDA approval process, wherein it has been demonstrated that the new drug is unaltered within that operating window. During production of the new drug, any deviation from the operating window results in the discarding of that batch of drug. Thus, a technique that permits more data to be generated by conducting experiments with wider parameter variation and thus, a wider operating window, could be a significant economic benefit to companies which currently have to discard batches of drugs.
Currently, for bioprocess development and optimization in the pharmaceutical industry, significant numbers of fermentations are needed under varying environmental and nutritional conditions. This is expensive and time-consuming in practice, as this type of research is typically performed in shake flasks (with practically no control of the bioprocess parameters) or in 1- to 100-liter laboratory scale bioreactors (Tholudur, A. et al., Biotechnol. Bioeng. 66: 1–16 (1999)). To decrease the number of experiments required for optimization, mathematical modeling is used (Alvarez-Ramirez, J. et al., J. Chem. Technol. Biotechnol. 74: 78–84 (1999); Boon, M. A. et al., Biotechnol Bioeng. 64: 558–567 (1999); Cooney, M. J. et al., Biotechnol. Prog. 15: 898–910 (1999); Tholudur A. et al., Biotechnol. Bioeng. 66: 1–16 (1999)). However, this approach also requires a significant number of fermentations for establishing process parameters. Further, currently available laboratory scale bioreactors are expensive and bulky, thus making bioprocess development and optimization inefficient as large numbers of simultaneous experiments cannot be conducted.
To overcome the bulky aspect of scale bioreactors, miniaturized bioreactors have been used (Walther, I. et al., Engine and Microbial Technol. 27: 778–783 (2000)). However, in small volumes, e.g., 1–2 ml or less, it is difficult, if not impossible, to use standard industrial probes for culture monitoring due to the probes' physical dimensions. Another problem is that standard Clark-type oxygen probes consume oxygen (Lee, Y. H. and Tsao, G. T., Advances in Biochemical Engineering, Ghose, T. K. et al. (eds.), Berlin, Springer-Verlag, p. 35 (1979); Bambot, S. B. et al., Biotech. Bioeng. 43: 1139–1145 (1994)). In small volumes, such probes compete with the cells for oxygen which distorts the results from the bioprocess. In addition, over time, drifts in calibration can occur (Bambot, S. B. et al., Biotech. Bioeng. 43: 1139–1145 (1994)). Miniaturized versions of standard industry probes are known (Liu, C. C. and Neuman, M. R., Diabetes Care 5: 275–277 (1982); Suzuki, H. et al., Biosens. Bioelectron. 6: 395–400 (1991); Zhong, L. et al., Chin. J. Biotechnol. 8: 57–65 (1992)). However, their use in bioprocessing is not economically feasible due to the sophisticated and expensive techniques required to manufacture the miniaturized probes. Thus, a fast, reliable and inexpensive bioprocessing system and technique, wherein experiments can be performed, optionally in parallel, in small or large volumes, with on-line measurement and control of multiple process parameters, is strongly desirable.